脉搏信号降噪

故障行波波头识别：模极大值原理识别故障行波波头(Modulus maxima principles identified wave head)

小波变换，反变换，以及提升小波变换，包括97小波，及53小波(Wavelet transform, inverse transform, as well as the lifting wavelet transform, including the 97 wavelet, and 53 wavelet)

通过设计Visual C程序源码对简单易懂的一维信号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波与Haar小波变换，从而的到小波分解系数；再通过改变分解的到的各层高频系数数进行信号的小波重构达到消噪噪的目的。在这一程序源码实现的过程中能直观地理解信号小波分解重构的过程与在信号消噪中的重要作用，和在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。 可直接 (Design Visual C program source code on a simple one-dimensional signal with a Gaussian white noise Daubechies wavelet, Morlet wavelet and Haar wavelet transform, and thus to the wavelet coefficients and then change the decomposition to each storey wavelet reconstruction frequency coefficient of the number of signal to noise canceling noise. In the process of realization of this program source code can be intuitively understood the signal wavelet decomposition and reconstruction process and an important role in signal denoising, and the selection coefficient in the high frequency coefficients of the layers of the weight of processing noise cancellation signal impact. Can be directly)

基于Matlab的JPEG2000图像压缩实现，离散小波变换DWT，程序中图片的位置修改下可以自己运行程序看到效果(Matlab-based implementation of the JPEG2000 image compression, discrete wavelet transform DWT, where the image changes the program may be run its own program to see results)

bandelet denoising toolbox for matlab

小波变换程序，用于对一维信号，二维信号进行稀疏变换(wavelet transform)

自己编写的基于指数遗忘快速算法改进的morlet小波变换程序(I have written forgotten fast algorithm based on the index improved wavelet transform morlet procedure)

本代码给出了图像的二维离散小波变换分解、重构图像以及误差图像。(The code gives the two-dimensional image decomposition of discrete wavelet transform, image reconstruction and the error image.)

实现盲源信号的分离，在一个信号源中提取出有用的信号。(Blind source signal separation, in a signal source to extract useful signals.)

对于非平稳信号，小波多尺度分解师一种有效的信号去噪方法，在小波去噪方法的基础上，提出了基于Lipschitz指数的小波阈值去噪方法(For non-stationary signals, signal de-noising of wavelet multi-scale decomposition ' is an effective method, wavelet denoising method based on proposed wavelet thresholding denoising method based on the Lipschitz exponent)